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Module airbyte_agent_sdk.connectors.zendesk_chat.connector

Zendesk-Chat connector.

Classes​

AccountsQuery(connector: ZendeskChatConnector) : Query class for Accounts entity operations.

Initialize query with connector reference.

Methods​

get(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Account : Returns the account information for the authenticated user

Returns: Account

AgentTimelineQuery(connector: ZendeskChatConnector) : Query class for AgentTimeline entity operations.

Initialize query with connector reference.

Methods​

list(self, start_time: int | None = None, limit: int | None = None, fields: str | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResultWithMeta[list[AgentTimeline], AgentTimelineListResultMeta] : List agent timeline (incremental export)

Args: start_time: Parameter start_time limit: Parameter limit fields: Parameter fields **kwargs: Additional parameters

Returns: AgentTimelineListResult

AgentsQuery(connector: ZendeskChatConnector) : Query class for Agents entity operations.

Initialize query with connector reference.

Methods​

context_store_search(self, query: AgentsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[AgentsSearchData] : Search agents records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (AgentsSearchFilter):

  • id: Unique agent identifier
  • email: Agent email address
  • display_name: Agent display name
  • first_name: Agent first name
  • last_name: Agent last name
  • enabled: Whether agent is enabled
  • role_id: Agent role ID
  • departments: Department IDs agent belongs to
  • create_date: When agent was created

Args: query: Filter and sort conditions. Supports operators such as eq, neq, gt, gte, lt, lte, in, startswith, endswith, contains, array_contains, fuzzy, keyword, not, and, or. Example: {"filter": {"eq": {"status": "active"}}} limit: Maximum results to return (default 1000) cursor: Pagination cursor from previous response's meta.cursor fields: Field paths to include in results. Each path is a list of keys for nested access. Example: [["id"], ["user", "name"]] returns id and user.name fields.

Returns: AgentsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

context_store_sql_query(self, sql: str, limit: int | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[dict[str, Any]] : Run a SQL query against agents records in the Airbyte Context Store.

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

get(self, agent_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Agent : Get an agent

Args: agent_id: Parameter agent_id **kwargs: Additional parameters

Returns: Agent

list(self, limit: int | None = None, since_id: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[list[Agent]] : List all agents

Args: limit: Parameter limit since_id: Parameter since_id **kwargs: Additional parameters

Returns: AgentsListResult

BansQuery(connector: ZendeskChatConnector) : Query class for Bans entity operations.

Initialize query with connector reference.

Methods​

get(self, ban_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Ban : Get a ban

Args: ban_id: Parameter ban_id **kwargs: Additional parameters

Returns: Ban

list(self, limit: int | None = None, since_id: int | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[dict[str, Any]] : List all bans

Args: limit: Parameter limit since_id: Parameter since_id **kwargs: Additional parameters

Returns: BansListResult

ChatsQuery(connector: ZendeskChatConnector) : Query class for Chats entity operations.

Initialize query with connector reference.

Methods​

context_store_search(self, query: ChatsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[ChatsSearchData] : Search chats records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (ChatsSearchFilter):

  • id: Unique chat identifier
  • timestamp: Chat start timestamp
  • update_timestamp: Last update timestamp
  • department_id: Department ID
  • department_name: Department name
  • duration: Chat duration in seconds
  • rating: Satisfaction rating
  • missed: Whether chat was missed
  • agent_ids: IDs of agents in chat

Args: query: Filter and sort conditions. Supports operators such as eq, neq, gt, gte, lt, lte, in, startswith, endswith, contains, array_contains, fuzzy, keyword, not, and, or. Example: {"filter": {"eq": {"status": "active"}}} limit: Maximum results to return (default 1000) cursor: Pagination cursor from previous response's meta.cursor fields: Field paths to include in results. Each path is a list of keys for nested access. Example: [["id"], ["user", "name"]] returns id and user.name fields.

Returns: ChatsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

context_store_sql_query(self, sql: str, limit: int | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[dict[str, Any]] : Run a SQL query against chats records in the Airbyte Context Store.

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

get(self, chat_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Chat : Get a chat

Args: chat_id: Parameter chat_id **kwargs: Additional parameters

Returns: Chat

list(self, start_time: int | None = None, limit: int | None = None, fields: str | None = None, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResultWithMeta[list[Chat], ChatsListResultMeta] : List chats (incremental export)

Args: start_time: Parameter start_time limit: Parameter limit fields: Parameter fields **kwargs: Additional parameters

Returns: ChatsListResult

DepartmentsQuery(connector: ZendeskChatConnector) : Query class for Departments entity operations.

Initialize query with connector reference.

Methods​

context_store_search(self, query: DepartmentsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[DepartmentsSearchData] : Search departments records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (DepartmentsSearchFilter):

  • id: Department ID
  • name: Department name
  • enabled: Whether department is enabled
  • members: Agent IDs in department

Args: query: Filter and sort conditions. Supports operators such as eq, neq, gt, gte, lt, lte, in, startswith, endswith, contains, array_contains, fuzzy, keyword, not, and, or. Example: {"filter": {"eq": {"status": "active"}}} limit: Maximum results to return (default 1000) cursor: Pagination cursor from previous response's meta.cursor fields: Field paths to include in results. Each path is a list of keys for nested access. Example: [["id"], ["user", "name"]] returns id and user.name fields.

Returns: DepartmentsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

context_store_sql_query(self, sql: str, limit: int | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[dict[str, Any]] : Run a SQL query against departments records in the Airbyte Context Store.

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

get(self, department_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Department : Get a department

Args: department_id: Parameter department_id **kwargs: Additional parameters

Returns: Department

list(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[list[Department]] : List all departments

Returns: DepartmentsListResult

GoalsQuery(connector: ZendeskChatConnector) : Query class for Goals entity operations.

Initialize query with connector reference.

Methods​

get(self, goal_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Goal : Get a goal

Args: goal_id: Parameter goal_id **kwargs: Additional parameters

Returns: Goal

list(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[list[Goal]] : List all goals

Returns: GoalsListResult

RolesQuery(connector: ZendeskChatConnector) : Query class for Roles entity operations.

Initialize query with connector reference.

Methods​

get(self, role_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Role : Get a role

Args: role_id: Parameter role_id **kwargs: Additional parameters

Returns: Role

list(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[list[Role]] : List all roles

Returns: RolesListResult

RoutingSettingsQuery(connector: ZendeskChatConnector) : Query class for RoutingSettings entity operations.

Initialize query with connector reference.

Methods​

get(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.RoutingSettings : Get routing settings

Returns: RoutingSettings

ShortcutsQuery(connector: ZendeskChatConnector) : Query class for Shortcuts entity operations.

Initialize query with connector reference.

Methods​

context_store_search(self, query: ShortcutsSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[ShortcutsSearchData] : Search shortcuts records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (ShortcutsSearchFilter):

  • id: Shortcut ID
  • name: Shortcut name/trigger
  • message: Shortcut message content
  • tags: Tags applied when shortcut is used

Args: query: Filter and sort conditions. Supports operators such as eq, neq, gt, gte, lt, lte, in, startswith, endswith, contains, array_contains, fuzzy, keyword, not, and, or. Example: {"filter": {"eq": {"status": "active"}}} limit: Maximum results to return (default 1000) cursor: Pagination cursor from previous response's meta.cursor fields: Field paths to include in results. Each path is a list of keys for nested access. Example: [["id"], ["user", "name"]] returns id and user.name fields.

Returns: ShortcutsSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

context_store_sql_query(self, sql: str, limit: int | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[dict[str, Any]] : Run a SQL query against shortcuts records in the Airbyte Context Store.

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

get(self, shortcut_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Shortcut : Get a shortcut

Args: shortcut_id: Parameter shortcut_id **kwargs: Additional parameters

Returns: Shortcut

list(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[list[Shortcut]] : List all shortcuts

Returns: ShortcutsListResult

SkillsQuery(connector: ZendeskChatConnector) : Query class for Skills entity operations.

Initialize query with connector reference.

Methods​

get(self, skill_id: str, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.Skill : Get a skill

Args: skill_id: Parameter skill_id **kwargs: Additional parameters

Returns: Skill

list(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[list[Skill]] : List all skills

Returns: SkillsListResult

TriggersQuery(connector: ZendeskChatConnector) : Query class for Triggers entity operations.

Initialize query with connector reference.

Methods​

context_store_search(self, query: TriggersSearchQuery, limit: int | None = None, cursor: str | None = None, fields: list[list[str]] | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[TriggersSearchData] : Search triggers records from Airbyte cache.

This operation searches cached data from Airbyte syncs. Only available in hosted execution mode.

Available filter fields (TriggersSearchFilter):

  • id: Trigger ID
  • name: Trigger name
  • enabled: Whether trigger is enabled

Args: query: Filter and sort conditions. Supports operators such as eq, neq, gt, gte, lt, lte, in, startswith, endswith, contains, array_contains, fuzzy, keyword, not, and, or. Example: {"filter": {"eq": {"status": "active"}}} limit: Maximum results to return (default 1000) cursor: Pagination cursor from previous response's meta.cursor fields: Field paths to include in results. Each path is a list of keys for nested access. Example: [["id"], ["user", "name"]] returns id and user.name fields.

Returns: TriggersSearchResult with typed records, pagination metadata, and optional search metadata

Raises: NotImplementedError: If called in local execution mode

context_store_sql_query(self, sql: str, limit: int | None = None) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.AirbyteSearchResult[dict[str, Any]] : Run a SQL query against triggers records in the Airbyte Context Store.

Only available in hosted execution mode.

Args: sql: SQL query to execute. limit: Maximum results to return.

Returns: AirbyteSearchResult containing the projected rows and query metadata.

Raises: NotImplementedError: If called in local execution mode.

list(self, **kwargs) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatExecuteResult[list[Trigger]] : List all triggers

Returns: TriggersListResult

ZendeskChatConnector(auth_config: ZendeskChatAuthConfig | AirbyteAuthConfig | BaseModel | None = None, on_token_refresh: Any | None = None, subdomain: str | None = None) : Type-safe Zendesk-Chat API connector.

Auto-generated from OpenAPI specification with full type safety.

Initialize a new zendesk-chat connector instance.

Supports both local and hosted execution modes:

  • Local mode: Provide connector-specific auth config (e.g., ZendeskChatAuthConfig)
  • Hosted mode: Provide AirbyteAuthConfig with client credentials and either connector_id or workspace_name

Args: auth_config: Either connector-specific auth config for local mode, or AirbyteAuthConfig for hosted mode on_token_refresh: Optional callback for OAuth2 token refresh persistence. Called with new_tokens dict when tokens are refreshed. Can be sync or async. Example: lambda tokens: save_to_database(tokens) subdomain: Your Zendesk subdomain (the part before .zendesk.com in your Zendesk URL) Examples:

Local mode (direct API calls)

connector = ZendeskChatConnector(auth_config=ZendeskChatAuthConfig(access_token="..."), subdomain="...")

Hosted mode with explicit connector_id (no lookup needed)

connector = ZendeskChatConnector( auth_config=AirbyteAuthConfig( airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789", connector_id="existing-source-uuid" ) )

Hosted mode with lookup by workspace_name

connector = ZendeskChatConnector( auth_config=AirbyteAuthConfig( workspace_name="user-123", organization_id="00000000-0000-0000-0000-000000000123", airbyte_client_id="client_abc123", airbyte_client_secret="secret_xyz789" ) )

Class variables​

connector_name : The type of the None singleton.

connector_version : The type of the None singleton.

sdk_version : The type of the None singleton.

Static methods​

agent_tool(role: AgentToolRole | None = None, *, inspect_tool: str | None = None, docs_tool: str | None = None, max_output_chars: int | None | Unset = UNSET, framework: FrameworkName = 'none', internal_retries: int = 0, should_internal_retry: Callable[[Exception, tuple[Any, ...], dict[str, Any]], bool] | None = None, exhausted_runtime_failure_message: Callable[[Exception, tuple[Any, ...], dict[str, Any]], str | None] | None = None) ‑> Callable[[~_F], ~_F] : Decorator for new user-written connector tool functions.

Use this when a tool needs a custom body or the framework lacks a native strategy. Instead of baking the full entity/action reference into the docstring, it instructs the agent to call this connector's inspect and docs tools before executing. Tool failures raise :class:airbyte_agent_sdk.AirbyteToolError by default (framework="none", no auto-detection) — pass framework=... to translate to a supported framework's signal instead.

Decorate three functions per connector — execute, inspect and docs. The role is inferred from each function's signature (extra parameters are allowed); a signature matching more than one role, a generic (*args, **kwargs) wrapper, or a callable whose signature cannot be read must pass the role explicitly:

  • (entity, action, ...) -> "execute"
  • (section, ...) -> "read_skill_docs"
  • () -> "inspect_connector"

Usage: connector = ZendeskChatConnector(...)

@ZendeskChatConnector.agent_tool() async def execute(entity: str, action: str, params: dict | None = None): return await connector.execute(entity=entity, action=action, params=params or {})

@ZendeskChatConnector.agent_tool() async def inspect_connector(): return await connector.inspect_connector()

@ZendeskChatConnector.agent_tool() async def read_skill_docs(section: str | None = None): return await connector.read_skill_docs(section)

Args: role: "execute" | "inspect_connector" | "read_skill_docs". None (default) infers the role from the decorated function's signature; an explicit role validates the canonical parameters are present (functions accepting **kwargs, or callables whose signature cannot be read, pass validation). inspect_tool: Exact registered name of the sibling inspect tool, woven into the execute docstring for tighter steering. Defaults to generic phrasing. docs_tool: Exact registered name of the sibling docs tool (see inspect_tool). max_output_chars: Max serialized output size before failing. Defaults per role: execute -> DEFAULT_MAX_OUTPUT_CHARS, docs tools -> None. framework: Translation target for tool failures. Defaults to "none" (raise AirbyteToolError); never auto-detects. internal_retries: How many transient runtime failures (429/5xx, network, timeout) to retry silently before surfacing. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions. should_internal_retry: Optional predicate (error, args, kwargs) -> bool further restricting which retryable errors are safe for this specific tool. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions. exhausted_runtime_failure_message: Optional callback (error, args, kwargs) -> str | None invoked after internal retries are exhausted or skipped. Forwarded to :func:airbyte_agent_sdk.translation.translate_exceptions.

tool_utils(func: _F | None = None, *, update_docstring: bool = True, max_output_chars: int | None = 100000, framework: FrameworkName | None = None, internal_retries: int = 0, should_internal_retry: Callable[[Exception, tuple[Any, ...], dict[str, Any]], bool] | None = None, exhausted_runtime_failure_message: Callable[[Exception, tuple[Any, ...], dict[str, Any]], str | None] | None = None) ‑> ~_F | Callable[[~_F], ~_F] : Deprecated. Add connector-specific documentation and runtime safeguards to one tool.

Kept for backwards compatibility with existing single-tool integrations; it is not removed and does not warn at runtime, but new code should use build_connector_tools or agent_tool below.

For new agents, prefer build_connector_tools. It returns progressive inspect_connector, read_skill_docs, and execute tools so the agent can load only the connector guidance it needs:

from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

When a new integration needs custom tool bodies or a framework without native support, use agent_tool instead.

Legacy: one generated-description tool​

Existing integrations can keep using tool_utils for one broad execute tool with the connector's full generated catalog in its description:

from fastmcp import FastMCP

connector = ZendeskChatConnector()
mcp = FastMCP("Connector Agent")

@mcp.tool()
@ZendeskChatConnector.tool_utils
async def execute(entity: str, action: str, params: dict):
    ...

Configure documentation, output limits, framework translation, and retries when needed:

@mcp.tool()
@ZendeskChatConnector.tool_utils(update_docstring=False, max_output_chars=None)
async def execute(entity: str, action: str, params: dict):
    ...

@mcp.tool()
@ZendeskChatConnector.tool_utils(framework="pydantic_ai", internal_retries=2)
async def execute(entity: str, action: str, params: dict):
    ...

This decorator composes translate_exceptions for runtime wrapping, output-size checks, framework signal translation, and optional internal retries, then adds connector-specific docstring augmentation.

Args: update_docstring: When True, append connector capabilities to __doc__. max_output_chars: Max serialized output size before raising. Use None to disable. framework: One of "pydantic_ai" | "langchain" | "openai_agents" | "mcp" | "none". Defaults to None, which auto-detects each framework's canonical import in order and falls back to "none" with a warning when no supported framework is installed. Explicit always wins, and an explicit framework whose package is missing raises RuntimeError. internal_retries: How many transient runtime failures (429/5xx, network, timeout) to retry silently before surfacing. Default 0. Forwarded to airbyte_agent_sdk.translation.translate_exceptions. should_internal_retry: Optional predicate (error, args, kwargs) -> bool further restricting which retryable errors are safe for this specific tool. Forwarded to airbyte_agent_sdk.translation.translate_exceptions. exhausted_runtime_failure_message: Optional callback (error, args, kwargs) -> str | None. Invoked after internal retries are exhausted or were skipped because should_internal_retry returned False. Forwarded to airbyte_agent_sdk.translation.translate_exceptions.

Instance variables​

connector_id: str | None : Get the connector/source ID (only available in hosted mode).

Returns: The connector ID if in hosted mode, None if in local mode.

Methods​

check(self) ‑> airbyte_agent_sdk.connectors.zendesk_chat.models.ZendeskChatCheckResult : Perform a health check to verify connectivity and credentials.

Executes a lightweight list operation (limit=1) to validate that the connector can communicate with the API and credentials are valid.

Returns: ZendeskChatCheckResult with status ("healthy" or "unhealthy") and optional error message

Example: result = await connector.check() if result.status == "healthy": print("Connection verified!") else: print(f"Check failed: {result.error}")

close(self) : Close the connector and release resources.

entity_schema(self, entity: str) ‑> dict[str, typing.Any] | None : Get the JSON schema for an entity.

Args: entity: Entity name (e.g., "contacts", "companies")

Returns: JSON schema dict describing the entity structure, or None if not found.

Example: schema = connector.entity_schema("contacts") if schema: print(f"Contact properties: {list(schema.get('properties', {}).keys())}")

execute(self, entity: str, action: "Literal['get', 'list', 'context_store_search', 'context_store_sql_query']", params: Mapping[str, Any] | None = None, *, select_fields: list[str] | None = None, exclude_fields: list[str] | None = None, skip_truncation: bool = True) ‑> Any : Execute an entity operation with full type safety.

This is the recommended interface for blessed connectors as it:

  • Uses the same signature as non-blessed connectors
  • Provides full IDE autocomplete for entity/action/params
  • Makes migration from generic to blessed connectors seamless

Args: entity: Entity name (e.g., "customers") action: Operation action (e.g., "create", "get", "list") params: Operation parameters (typed based on entity+action) select_fields: Optional allowlist of dot-notation fields to include exclude_fields: Optional blocklist of dot-notation fields to remove skip_truncation: Disable long-text truncation for collection actions

Returns: Typed response based on the operation

Example: customer = await connector.execute( entity="customers", action="get", params={"id": "cus_123"} )

inspect_connector(self) ‑> dict[str, typing.Any] : Inspect this connector's hosted metadata/readiness and resolve its docs skill id.

Call this before read_skill_docs in the normal hosted flow. For local/offline connectors this returns a local-mode payload with a warning instead of a hosted inspection.

Example: info = await connector.inspect_connector() print(info["docs_skill_id"])

list_entities(self) ‑> list[dict[str, typing.Any]] : Get structured data about available entities, actions, and parameters.

Returns a list of entity descriptions with:

  • entity_name: Name of the entity (e.g., "contacts", "deals")
  • description: Entity description from the first endpoint
  • available_actions: List of actions (e.g., ["list", "get", "create"])
  • parameters: Dict mapping action -> list of parameter dicts

Example: entities = connector.list_entities() for entity in entities: print(f"{entity['entity_name']}: {entity['available_actions']}")

read_skill_docs(self, section: str | None = None) ‑> str : Read this connector's usage docs, rendered to text.

Omit section for the outline and general guidance; pass an exact section id from the outline for full details. For local/offline connectors the full generated docs are returned and section is ignored.

Example: outline = await connector.read_skill_docs() details = await connector.read_skill_docs(section="entity:contacts")

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